Sirgiovanni I, Avignone S, Groppo M et al (2014) Intracranial haemorrhage: an incidental finding at magnetic resonance imaging in a cohort of late preterm and term infants. Pediatr Radiol 44:289–296
Hausman-Kedem M, Libzon S, Fattal Valevski A et al (2025) Clinical and neuroimaging patterns of perinatal intracranial haemorrhage in fetuses and term-born neonates: a prospective observational cohort study. Arch Dis Child Fetal Neonatal Ed 110(3):303–312
Tan AP, Svrckova P, Cowan F et al (2018) Intracranial haemorrhage in neonates: a review of etiologies, patterns and predicted clinical outcomes. Eur J Paediatr Neuro 22:690–717
JJ V (2008) Intracranial haemorrhage: germinal matrix-intraventricular haemorrhage of the premature infant. In: Volpe JJ (ed) Neurology of the newborn. 5th ed. Philadelphia: Saunders Elsevier
Davis AS, Hintz SR, Goldstein RF et al (2014) Outcomes of extremely preterm infants following severe intracranial haemorrhage. J Perinatol 34:203–208
Mukerji A, Shah V, Shah PS (2015) Periventricular/intraventricular haemorrhage and neurodevelopmental outcomes: a meta-analysis. Pediatrics (Evanston) 136:1132
Bolisetty S, Dhawan A, Abdel-Latif M et al (2014) Intraventricular haemorrhage and neurodevelopmental outcomes in extreme preterm infants. Pediatrics (Evanston) 133:55
Roelants-van RA, Groenendaal F, Beek FJ et al (2001) Parenchymal brain injury in the preterm infant: comparison of cranial ultrasound, MRI and neurodevelopmental outcome. Neuropediatrics 32:80–89
Zhang Q, Zhou X (2023) Review on the application of imaging examination for brain injury in premature infants. Front Neurol 14:1100623
PubMed PubMed Central Google Scholar
Obeid R, Tabrizi PR, Mansoor A et al (2019) Ventricular shape evaluation on early ultrasound predicts post-hemorrhagic hydrocephalus. Pediatr RES 85:293–298
Ahmad T, Guida A, Stewart S et al (2024) Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants? Eur Radiol
Ciceri T, Squarcina L, Giubergia A et al (2023) Review on deep learning fetal brain segmentation from magnetic resonance images. Artif Intell Med 143:102608
Karimi D, Rollins CK, Velasco-Annis C et al (2023) Learning to segment fetal brain tissue from noisy annotations. Med Image Anal 85:102731
PubMed PubMed Central Google Scholar
Zhu J, Yao S, Yao Z et al (2023) White matter injury detection based on preterm infant cranial ultrasound images. Front Pediatr 11:1144952
PubMed PubMed Central Google Scholar
Naeem A, Anees T, Naqvi RA et al (2022) A Comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis. J Personalized Med 12:275
Umapathy S, Murugappan M, Bharathi D et al (2023) Automated computer-aided detection and classification of intracranial haemorrhage using ensemble deep learning techniques. Diagnostics (Basel, Switzerland) 13(18):2987
Kim KY, Nowrangi R, McGehee A et al (2022) Assessment of germinal matrix haemorrhage on head ultrasound with deep learning algorithms. Pediatr Radiol 52:533–538
Cohen JF, Korevaar DA, Altman DG et al (2016) STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 6:e12799
Inder TE, Perlman JM, Volpe JJ (2018) Chapter 24 - preterm intraventricular haemorrhage/posthemorrhagic hydrocephalus. Volpe’s neurology of the newborn (Sixth Edition):637–698
Singh P, Mukundan R, De Ryke R (2020) Feature enhancement in medical ultrasound videos using contrast-limited adaptive histogram equalization. J Digit Imaging 33:273–285
Mei F, Zhang D, Yang Y (2020) Improved non-local self-similarity measures for effective speckle noise reduction in ultrasound images. Comput Meth Prog Bio 196:105670
Goceri E (2023) Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev 56:12561–12605
He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 770–778
Woo S et al (2018) CBAM: convolutional block attention module. Computer Vision–ECCV 2018, pp 3–19
Lin TY, Goyal P, Girshick R et al (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42:318–327
Zhang YY, Mao HM, Wei CG et al (2024) Development and validation of a biparametric MRI deep learning radiomics model with clinical characteristics for predicting perineural invasion in patients with prostate cancer. Acad Radiol 31:5054–5065
Wang Y, Gao J, Yin Z et al (2024) Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images. Front Oncol 14:1384105
PubMed PubMed Central Google Scholar
Qin X, Hu X, Xiao W et al (2023) Preoperative evaluation of hepatocellular carcinoma differentiation using contrast-enhanced ultrasound-based deep-learning radiomics model. J Hepatocell Carcinoma 10:157–168
PubMed PubMed Central Google Scholar
B Z, A K, A L et al (2016) Learning deep features for discriminative localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2921–2929
Zhou Y, Sharpee TO (2022) Using Global t-SNE to Preserve Intercluster Data Structure. Neural Comput 34:1637–1651
PubMed PubMed Central Google Scholar
Ye H, Gao F, Yin Y et al (2019) Precise diagnosis of intracranial haemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 29:6191–6201
PubMed PubMed Central Google Scholar
Dawud AM, Yurtkan K, Oztoprak H (2019) Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput Intell Neurosci 2019:4629859
PubMed PubMed Central Google Scholar
Wang T, Song N, Liu L et al (2021) Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral haemorrhage volume measurement. Bmc Medimaging 21:125
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